Diagnosis of glaucoma by complex autoantibody repertoires in body fluids

ABSTRACT

The invention relates to a method for the diagnosis of glaucoma based on the composition of autoantibodies against ocular antigens in body fluids of individuals. The method is characterized in that in a first step, autoantibodies against retinal and/or optic nerve head antigens are detected and measured in body fluids of an individual, and, in a second step, the auto-antibody pattern is correlated with corresponding patterns of healthy individuals and glaucoma patients. The invention further relates to a method for assessing an individual&#39;s risk for developing glaucoma, and to kits for use in the method of the invention.

BACKGROUND OF THE INVENTION

Glaucoma, one of the leading causes of blindness worldwide [1],represents a group of ocular disorders that are responsible for loss ofretinal ganglion cells and their axons, damage to the optic nerve, andgradual loss of visual field. People of all age-groups can be affectedby this disease. At the age of 70 about seven percent of the populationare suffering from glaucoma. Unlike other eye disorders, the causes ofglaucoma and the best way to treat them are still not completelyinvestigated. The optic nerve head is considered the earliest site ofnerve damage in glaucoma. The damage of these nerve fibers causesretinal ganglion cell death through retrograde degeneration. This canlead to total and irreversible blindness.

Although elevated intraocular pressure can be an important risk factorof glaucoma, it is no longer considered as an essential part of thedefinition of the disease, since some glaucomatous patients have normalintraocular pressures [2]. Only 76% of the patients with glaucoma havean intraocular pressure higher than 21 mm Hg [3]. Intraocular pressureof about 16% of glaucomatous eyes was never recorded above 21 mm Hg.Thus, a more effective diagnostic criterion is necessary.

Currently, the most important screening methods for glaucomatouspatients include tonometry, ophthalmoscopy and perimetry. Despite itswell-known limitations, measuring the intraocular pressure (tonometry)is the most common method for glaucoma screening. Tonometry has asensitivity and specificity of about 65 percent. Ophthalmoscopy is amethod for examining optic nerve head morphology. But the usefulness ofthis method is highly dependent on the skills of the examiner. Perimetrypermits diagnosis of visual field loss, which is an indicator forganglion cell death. But this method allows only detecting severe damageat an advanced stage of the disease. Therefore, a diagnostic method isneeded which is more objective and more sensitive and can be applied atan earlier stage in the evolution of the disease (i.e. beforeirreversible damage is manifest).

There are different kinds of glaucoma. Because the pathophysiology,clinical presence and treatment of the different types of glaucoma areso varied, there is no single definition that adequately encompasses allforms. The most common form is primary open-angle glaucoma (POAG),characterized by optic nerve damage and high intraocular pressure. Butthere is also a high number of patients who never had elevatedintraocular pressure; this form of glaucoma is usually called normaltension glaucoma (NTG). There are several other forms of primary andsecondary glaucoma. There is not only adult onset glaucoma but there arealso juvenile forms of glaucoma.

In some patients there is evidence for an autoimmune mechanism beingresponsible for glaucomatous injury to the optic nerve. Their glaucomaconstitutes an organ-specific autoimmune disease, characterized byimmune-mediated tissue destruction occurring in a limited range oftissue types or cells [4].

Several specific antibodies have been detected in serum samples ofglaucomatous patients.

The different families of heat shock proteins (HSPs) were detected insera of patients with glaucoma like POAG or NTG. Heat shock proteins canbe divided into different groups on the basis of their molecular weight,such as HSP-90 (90 kilo Dalton), HSP-70 (70 kDa), HSP-60 (60 kDa), andsmall heat shock proteins (25 to 30 kDa). Heat shock proteins, alsocalled stress proteins, serve as cellular chaperones [5] and asprotectors for the whole organism. Heat shock proteins appear everywherein normal tissue. By carrying immunogenic peptides, they are capable oftriggering an immune response. They are considered neuroprotective andare expressed as a response to stress conditions such as ischemia orexcitotoxicity. But people with reduced or elevated levels of heat shockproteins could be lacking this neuroprotective factor. This deficiencymight be a possible cause for the development of glaucoma. High titersof autoantibodies to heat shock proteins could lead to optic neuropathyin these patients [6]. In patients with glaucoma, elevated serumautoantibodies against small heat shock proteins have been found [7].These small heat shock proteins include α-crystallins (αA-crystallin andαB-crystallin subunits) and HSP-27. α-Crystallin is the predominantstructural protein of the ocular lens and is also found in retinalcells. Hsp-27 antibody can enter retinal cells and this can lead toapoptosis of these cells [8].

No association between the serum HSP antibody titers and optic discparameters or peripapillary atrophy parameters has been found [9].

Wax et al. developed an animal model to back up the thesis thatautoantibodies can be a cause of glaucoma [10]. Rats were immunized withHsp27 and showed a degeneration of neurons in the retinal ganglion celllayer.

Antibodies against human vimentin and human glial fibrillary acidprotein (GFAP) were detected in human optic nerve astrocytes exposed toelevated hydrostatic pressure [11]. The astrocytes developed anincreased synthesis and redistribution of vimentin and GFAP.

Anti-Ro/SS-A (Sjögren syndrome A; also commonly called Ro antigens)antibodies were detected in patients with normal tension glaucoma [12].

Romano et al. tested sera from glaucoma patients for anti-rhodopsinantibodies against retinal antigens [13]. Those patients had a prioriclinically diagnosed POAG or NTG. Western blots of these sera showedantibodies against a 40-kDa antigen which was later identified asrhodopsin.

Serum antibodies that bind to human optic nerve head proteoglycans,including chondroitin sulfate and heparin, are found in patients withglaucoma [14]. Glycosaminoglycans (GAGs) play an important role asmembrane proteins; they are components of extracellular matrix in theoptic nerve head.

Serum autoantibodies against gamma-enolase (γ-enolase) in retinalganglion cells of glaucomatous patients have been detected by Maruyamaet al. [15]. These antibody levels were elevated in patients withglaucoma and were found in approximately 20% of glaucomatous patients.γ-Enolase is a neuron-specific enolase.

Lymphocytes in the blood of glaucoma patients were examined for antigensby Yang et al. These analyses showed that sera from patients withglaucoma have elevated small interleukin-2 (sII-2) levels [16]. sII-2 isproduced by T-cells and increases the level of other cytokines andantibodies.

Glutathione S-transferase (GST) antibodies were detected in glaucomatouspatient sera [17]. These antibodies bound against bovine retina. GST isexpressed in tissue cytosols and membranes. It is present in the centralnervous system, the retina, and throughout the whole body.

Tumor necrosis factor (TNF)-α and TNF-α receptor-1 are elevated in theretina of glaucoma eyes compared to healthy donors [18]. Tezel et al.found TNF-α in glaucomatous eye was more intense in retinal areas closeto the optic nerve head than in the periphery. TNF-α production isincreased in retinal glial cells after exposure to elevated hydrostaticpressure or insulted ischemia [19]. High TNF-α levels lead to apoptosisby driving axonal degeneration [20]. Methods for treating glaucoma byinhibiting TNF-α are subject of patent application WO 01/58469.

Antiphosphatidylserine (APS) antibodies have been detected in patientswith normal tension glaucoma [21]. The NTG patients showed higher levelsthan healthy control subjects and patients with primary open angleglaucoma. Antiphosphatidylserine antibodies are a subspecies ofantiphospholipid antibodies (APL) and bind to phosphatidylserinemolecules. Other subspecies are cardiolipin (ACL) and β2-glycoprotein(β2GP). Through this cascade the antibodies may be responsible forcausing thrombosis. Cardiolipin binds to apoptotic cells which leads toan increased level of tumor necrosis factor-α (TNF-α). Therefore, theyplay an important role in thrombosis development.

Myocilin/TIGR is expressed in optic nerves and trabecular meshwork ofglaucomatous eyes [22]. TIGR is the former name of myocilin. Theresearchers observed a loss of myocilin in glaucomatous eyes. Itsexpression in the trabecular meshwork is thought to be responsible forthe elevated intraocular pressure associated with some forms of glaucoma[23]. Mutations of the TIGR gene increase the risk of early-onsetglaucoma. The presence of the apolipoprotein E (ApoE) allele is anindicative of an increased risk of developing early-onset glaucoma, seepatent application WO 00/68429.

A human transcription factor named FKHL7 is expressed most abundantly inthe eye. Patent application WO 99/53060 describes this factor and itsuse in treating a diagnosing glaucoma

Optineurin antibody has been detected in whole-cell extracts frompatients with adult-onset primary open-angle glaucoma [24]. Optineurinmay be part of the tumor necrosis factor-α signaling pathway. It isexpressed in trabecular meshwork, ciliary epithelium, and retina

γ-Synuclein proteins occur in the optic nerve of glaucomatous eyes in asignificantly higher level than in control eyes without glaucoma [25].Synucleins contribute to the pathology of neuronal degeneration. Allthree members of synucleins (α-, β-, and γ-synuclein) are expressed inretina and optic nerve. The bovine orthologue of γ-synuclein, synoretin,is mainly localized to the nuclear and synaptic regions of retinal cells[26].

Testing for serum autoantibodies in the clinical evaluation ofneuropathy syndromes is widely practiced. Autoantibodies can serve asmarkers in diagnosis and can lead to prognosis and treatment. Althoughsome auto-antibodies in the sera of glaucoma patients have beenidentified and correlated with the glaucoma disease, as describedhereinbefore, many still remain unknown.

Western blotting has surfaced as a powerful tool for detecting specificautoantibodies in immune diseases. Complicating the straightforwardidentification of pathogenically relevant antigens, however, is thatnormal sera contain large amounts of natural antibodies which manifestthemselves in complex staining patterns [27][28]. Thus, it cancomplicate the differentiation of disease-associated auto-antibodiesfrom the complex background of “auto-immune noise”, i.e. naturallyoccurring autoantibodies. Most of previous studies evaluated one or afew specific disease-related antibodies and have screened only a limitednumber of purified homologous or heterologous proteins as antigens bymeans of ELISA or RIA. A diagnosis based on these antibodies wasimpossible to establish. On the other hand Western blotting has evolvedas the most important tool to demonstrate autoantibodies in auto-immunediseases because it permits simultaneous screening for a wide spectrumof different auto-antigens. A recent new technique, capable of analyzingthese complex staining patterns of Western blots simultaneously, isbased on digital image analysis. This technique has been successfullyused in studies of myasthenia gravis, Graves' disease and experimentaluveitis [29][30][31].

SUMMARY OF THE INVENTION

The invention relates to a method for the diagnosis of glaucoma based onthe composition of autoantibodies against ocular antigens in body fluidsof an individual. The method is characterized in that in a first step,autoantibodies against ocular antigens are detected and measured in bodyfluids of an individual, and, in a second step, the autoantibody patternis correlated with corresponding patterns of healthy individuals andglaucoma patients.

The invention further relates to a method for assessing an individual'srisk for developing glaucoma with or without an increased intraocularpressure by analyzing the autoantibody repertoire in the individual'sbody fluids against ocular antigens as biomarkers for the diagnosis ofglaucoma.

The invention further relates to kits for use in the method of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

The invention relates to a method for the diagnosis of glaucoma based onthe composition of autoantibodies in body fluids of patients. The term“glaucoma”, as used herein, refers to all kind of primary open-angleglaucoma, including both juvenile-onset and adult- or late-onsetglaucoma, pseudoexfoliation (PEX) syndrome and PEX glaucoma, with orwithout an elevated intraocular pressure, and to normal pressureglaucoma (normal tension glaucoma, NTG, or low-tension glaucoma, LTG).

In the method of the invention, the complex autoantibody repertoires inbody fluids of patients are used to set up a diagnosis and prognosis forthe development and course of glaucoma. In the first step, the methoddetects the autoantibodies against ocular antigens, e.g. against retinalantigens, optic nerve antigens, optic nerve head antigens, trabecularmeshwork antigens, uveal antigens, or a mixture of them, in particularretinal antigens and optic nerve head antigens, in body fluids of anindividual. “Body fluids” includes all fluids containing antibodies suchas sera, tears, saliva, urine, aqueous humour, vitreous body of the eye,etc. Preferred body fluids in the method of the invention are sera andtears, in particular sera. In the second step, the autoantibody patternis correlated with corresponding patterns of healthy individuals andglaucoma patients, in particular patients with different types ofglaucoma. The invention also relates to the method of comparison ofcomplex autoantibody patterns by calculation, e.g. a method ofcomparison of complex autoantibody patterns by calculation wherein apattern of autoantibodies against ocular antigens of an individual iscompared with the corresponding autoantibody pattern of healthyindividuals and of glaucoma patients, e.g. patients with primaryopen-angle glaucoma or patients with normal tension glaucoma.

It may be noted that the invention relates to a comparison of anautoantibody pattern, i.e. a complex mixture of a large number ofautoantibodies both known (and discussed in the background section) andunknown. Under “large number” at least 10 autoantibodies, morepreferably at least 20, most preferably at least 30 autoantibodies areunderstood. It is of no relevance for the method of the inventionwhether the particular antibody or the antigens are properlycharacterized, since the procedure relies only on a molecular masscomparison.

Detection of autoantibody pattern may be by conventional Western blottechniques. However, the method of the invention also includes the useof other commercially available or experimental detection techniquessuch as chemiluminescence assays, ELISA, RIA, and techniques based onmicroarray chips, such as SELDI-TOF-type (surface-enhanced laserdesorption/ionization in time-of-flight mass spectrometry; availablee.g. under the trade name ProteinChip™ Array, from Ciphergen, Fremont,Calif., USA), matrix assisted laser desorption/ionization (MALDI) massspectroscopy, or other antibody-based chip array techniques (e.g. fromBD Biosciences Clontech, Heidelberg, Germany).

The SELDI-TOF technique allows mass screening for auto-antibodies in avery reliable, fast, and extremely sensitive manner. For example, theProteinChip™ system (Ciphergen) uses protein chip arrays and SELDI-TOFtechnology for capturing, detection, and analysis of proteins withoutlabelling and without the need for chemical modification. Themicro-scale design of the arrays allows the analysis of very smallquantities of proteins. Arrays with biologically activated surfaces areused that permit antibody capture studies. Preferably, protein-A chipsare incubated with sera of patients, then treated with a complexsolution of auto-antigens, i.e. ocular antigens. If the protein-A boundautoantibodies recognize their antigens, these proteins can be separatedby their molecular masses and detected by mass spectrometry. At highermolecular weights (>30 kDa) the detection sensitivity of this on-chipmethod is comparable to conventional Western blotting. At lowermolecular weights, the sensitivity of the Western blot technique iseasily surpassed by the on-chip method. The on-chip procedure is easy touse, less time consuming than Western blotting, and more sensitive atleast in the low molecular weight range. Furthermore, theantigen-antibody reactions can be performed using beads bindingautoantibodies. After elution of the antigens bound by antigen-antibodyreactions from the beads, they can be analyzed using SELDI-TOF chips orconventional electrophoretical techniques.

Antibody-based chip arrays (e.g. Clontech) facilitate the diagnosisprocess by just spotting onto a micro-chip appropriate ocular antigensthat subsequently are recognized by their antibodies in the sera ofpatients.

After detection, the complex autoantibody repertoires of body fluids areread in a digital image analysis system or other device fordigitization, and subsequently analyzed by multivariate statisticaltechniques, e.g. analysis of discriminance, classification/regressiontrees, and/or artificial neural networks. Artificial neural networkslearn from experience, not from programming. They are fast, tolerant ofimperfect data, and do not need formulas or rules. Artificial neuralnetworks are able to generalize and extract consistent features ofpatterns used to train them. In the present invention, the artificialneural network is “trained” to recognize the antibody patterns ofglaucoma patients. The preferred artificial neural network used in theinvention is the multiple layer feedforward network (MLFN) with thebackpropagation training algorithm. This kind of network is typicallyformed by three layers: The input layer receives information from the“external world”. The output layer presents the results to the connecteddevices. A layer of hidden neurons is sandwiched between them. Networksare trained by presenting known samples to them. The network attempts tochange the function (weight) of each neuron until all training samplesare classified correctly.

Other types of artificial neural networks may be used, e.g.self-propagation procedures, probalistic neural networks, other kind oftraining algorithms, pruning techniques, and genetic algorithms.

These data analysis techniques differentiate between healthy individualsand glaucoma patients by considering the whole complex antibody patternof each patient. By comparing the antibody pattern of a patient withsuspected glaucoma with the antibody pattern in samples of otherglaucoma patients and healthy individuals, the method can calculate towhich clinical group the autoantibody pattern of a patient with unknownpathology reveals the highest similarity.

The method of the invention not only includes the computationaltechniques as demonstrated herein, but also similar technologies, e.g.the use of other pattern matching techniques, other classifyingstatistical techniques or other methods to acquire the antibody-antigenreaction data of individuals.

In this complex antibody repertoire used, some of the antigen-antibodyreactions are already known and described by others, as described in thebackground section. Further antibody-antigen interactions analyzed withthe method of the invention are still unknown.

Several antigen-antibody reactions are significantly higher in healthyindividuals than in glaucoma patients. Reactivities which are absent inglaucoma patients are also used in the analysis of the antibodyrepertoires according to this invention. They contribute to highersensitivity and specificity of the diagnostic method. The use ofautoantibodies that are not present or have or lower reactivity inauto-immune patients compared to controls for diagnosing auto-immunediseases is also part of this invention.

The method of this invention uses the increase or decrease ofautoantibodies in diseased patients, compared to controls, as“biomarkers” for the diagnosis of the disease. This implies thatknowledge about the identity of those antibodies is not needed for areliable diagnosis of the disease. No single autoantibody reliablydistinguishes glaucoma patients from healthy individuals.

Although the method is described for the diagnosis of glaucoma patients,it can also be used for the assessment of a (healthy) individual's riskfor developing glaucoma with or without an elevated intraocularpressure. Furthermore, the described method is useful for assessment ofprogression and/or severeness of glaucoma. For that purpose the changein the antibody pattern over time and corresponding computational andpattern matching techniques are used.

Kits for diagnosis of glaucoma according to the invention are based onconventional Western blotting technique, on protein chip approaches orother techniques useful in the method of this invention. A kit maycomprise a ready-to-use antigen mixture, chemicals and materials neededto perform the biochemical analysis, e.g. Western blots or biochips,and, optionally, a bundled computer software including the specializedalgorithm to detect glaucoma in the antibody pattern of patients. Kitsaccording to the invention may be for use by a doctor or even a patienton its own, or for a professional diagnostic service laboratory center.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

DESCRIPTION OF THE FIGURES

FIG. 1: Antigen-antibody reactions from Western blots of severalpatients. The intensity of staining reaction of a single Western blot(z-axis, arbitrary scanner units U) is plotted vs. the molecular weight(x-axis, kilo-Daltons kDa) and the patient (y-axis). Patients belong tothe clinical groups primary open-angle glaucoma (POAG), normal tensionglaucoma (NTG), and healthy subjects (CTRL).

FIG. 2: Antigen-antibody reactions against retinal antigens from Westernblots plotted (Scanner units U) vs. their molecular weight (kilo-DaltonskDa, x-axis).

1: healthy control subjects (CTRL); 2: patients with primary open-angleglaucoma (POAG); 3: patients with normal tension glaucoma (NTG).

1: healthy control subjects (CTRL); 2: patients with primary open-angleglaucoma (POAG); 3: patients with normal tension glaucoma (NTG).

FIG. 3: Antigen-antibody reactions against optic nerve head antigensfrom Western blots plotted (Scanner units U) vs. their molecular weight(kilo-Daltons kDa, x-axis).

1: healthy control subjects (CTRL); 2: patients with primary open-angleglaucoma (POAG); 3: patients with normal tension glaucoma (NTG).

FIG. 4: ROC (responder operating characteristic) scatter plot(true-positive vs. false positive) for the diagnosis of glaucoma bymeans of complex auto-antibody repertoires. x-axis: specificity, y-axis:sensitivity. The calculation of the area under curve (AUC) results in anr-value of 0.92 (which is a very good diagnostic test).

FIG. 5: Analysis of antigen-antibody reactions in patients with primaryopen-angle and normal tension glaucoma (GL) and healthy subjects (CTRL)based on protein-chip technologies. The autoantibodies in sera ofpatients are captured by Ciphergen IDM-beads, incubated with ocularantigens and measured by the ProteinChip-Reader (time-of-flight (TOF)mass spectrometry). Scanner units U (y-axis) vs. molecular weight(kilo-Dalton kDa, x-axis). NULL=No antigens used in analysis sample.

EXAMPLES

A total of 524 patients are divided into analysis groups: healthyvolunteers without any ocular disorders (CTRL, n=189), patients withprimary open angle glaucoma (POAG, n=96), and normal tension glaucoma(NTG, n=74). According to the classification guidelines of the EuropeanGlaucoma Society, patients suffering from ocular hypertension (OHT,n=87) without any glaucoma damage are classified as healthy controls andare included in the CTRL group in this study. To test the robustness ofthe glaucoma detection, in an additional procedure 165 patients withother ocular disorders (e.g. retinal diseases) are included in thenon-glaucoma group (CTRL2).

The inclusion criteria for POAG are: intraocular pressure (IOP) morethan 21 mm Hg, untreated, on at least one occasion. No known alternativereason for elevated IOP like alternative causes of optic neuropathy(e.g. infection, inflammation, ischemic disease, and compressivelesions). Intraocular pressure is determined by Goldmann applanationtonometer, and visual field is examined by Goldmann perimeter.

Criteria for OHT: no optic nerve cupping or visual field loss, butelevated IOP (=more than 23 mm Hg), and also open angles and the absenceof alternative causes of optic neuropathy.

CTRL group criteria: healthy subjects with no history of oculardisorders, no pathologic fundus, no elevated IOP, and no eye medication.

Exclusion criteria for all groups: acute attack of glaucoma, diabetesmellitus and retinopathy, retinal detachment, and retinal vascularobliteration.

All groups are matched for age and gender. After giving their informedconsent blood is taken from all patients. Those samples are centrifugedand the serum stored for later examination.

Western Blots

The sera of patients are tested against Western blots of retinal andoptic nerve antigens.

Retinas from bovine eyes are dissected. They are homogenized in samplebuffer (1 M Tris, pH 7.5; 10% SDS; DTT; bromophenol blue, pH 6.8), andseparated by centrifugation (15000 rpm for one hour). The samples arecooked and homogenized several times. The pellet is discarded and thesupernatant is stored for later analysis.

The retina extracts are used for 13.5% sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE) using MultiGel-Long(Biometra, Goettingen, Germany). After electrophoresis the gels aretransferred to nitrocellulose membrane Protran BA 83 (Schleicher andSchuell, Dassel, Germany) by using a Semi-Dry Blotter (Biometra,Goettingen, Germany). After blotting the membranes for one hour, thequality of the transfer is checked by staining the nitrocellulose withPonceau S solution (Sigma, Munich, Germany). The blots are blocked withblocking buffer (5% non-fat dry milk in phosphate-buffered saline (PBS))for one hour. The nitrocellulose is cut into 4 mm wide strips. One stripis used per patient. The strips are incubated over night with patientserum (1:25 dilution, in washing buffer). 1000 μl volume per strip isused. A negative control strip (as negative control one strip isincubated only with wash buffer and no serum) and a positive controlstrip (a sample with known antibody reactivity) are done for each blotto check the quality of the immunostaining. After washing the stripswith Tris-buffered saline (TBS) for several times, they are incubatedwith secondary antibody (peroxidase-conjugated Immuno Pure® GoatAnti-Human IgG (H+ L); diluted 1:500, Pierce, Ill., USA) for one hour.After several washing steps the bands are developed by 0.05%4-chloro-1-naphthol (Sigma, Munich, Germany) with 0.015% hydrogenperoxide in 20% methanol in TBS for 20 minutes.

Molecular weights are estimated for each band based on the distancemigrated for ten known molecular weight standards (BenchMark,Invitrogen, Karlsruhe, Germany).

Digital image analysis and evaluation of the densitometric data of theelectrophoretic separations are performed by means of the BioDocAnalyze™software package (Biometra, Goettingen, Germany). BioDocAnalyze™ createsdensitometric data files for each Western blot, which show thegray-intensity values (8-bit gray values) versus the Rf values (relativemobility, x-axis). BioDocAnalyze™ evaluates the height, area, molecularweight, Rf value, etc. of all peaks in this densitometric data file andalso includes a photographic quality half tone bitmap.

From each densitometric data file, two data vectors are built in thefollowing way: First, the Rf axis (=molecular weight) is broken into1000 classes such that each variable of the vector represents 1/1000 ofthe molecular weight region. For each molecular weight of this 1000class data vector the corresponding grey value of the densitograph ofeach western blot, which represents the intensity of theantigen-antibody reaction, is calculated and normalized according to theentire area und curve. Thus, each variable of the data vector representsthe percent area of the peaks of the electrophoretic lane at thecorresponding Rf region.

Furthermore, another data vector with a list of all peak found in eachWestern blot is built. For each peak, the area under curve and theintensity is recorded. The data vectors are compiled into a database forsubsequent calculations, and each of them is randomly divided in twosubsets: The test (unknown data, not used in the calculation procedure)and the training set (known data, used in the calculation procedure).

Calculation Procedures

First, for all Western blots in the database the algorithms group thepeaks into clusters. For each peak, which is below a given threshold,the algorithm searches in all other Western blots if at this specificmolecular weight region a peak can be found. In this way, a list ofpeaks is built revealing for all Western blot lanes the existence (andintensity) of peaks for all molecular weight regions where in more than10% of lanes peaks could be detected. Using this generated list of peakclusters, multivariate algorithms such as analysis of discriminance orclassification trees can be performed to analyze the complex bandingpatterns.

Multivariate analysis of discriminance: As described above, for eachWestern blot data vectors are exported and peak clusters are built. Eachdata file is assigned to solely one of both predefined groups: “healthy”or “glaucoma”. The analysis of discriminance can test thenull-hypothesis that data vectors of the groups arise from amultivariate normally-distributed population.

Artificial Neural Network (ANN): The multiple layer feedforward network(MLFN) with the backpropagation training algorithm is used as providedin the Statistica 6.0 software package (StatSoft, Tulsa, USA). All datavectors are presented to the network for the learning process.

All patients exhibit different, complex staining patterns ofautoantibodies against retinal antigens and optic nerve head antigens.FIG. 1 reveals the complex antibody patterns of several patients. Thenumber of peaks of Western blots against retinal antigens is increasedin sera of POAG patients compared to all other groups. Including allpeaks the analysis of discriminance reveals a statistically significantdifference between the patterns of POAG and NTG compared to all othergroups (P<0.01). FIGS. 2 and 3 demonstrate the mean data vectors ofantigen-antibody reactions for the clinical groups POAG, NTG, andcontrols (CTRL).

If the control group is divided into “normal” and “ocular hypertensionwithout glaucoma (OHT)”, a statistically different antibody pattern inOHT patients compared to controls is found. Thus, the analysis ofantibody pattern could not only be used to detect glaucoma, but also beuseful in understanding the phenomenon of ocular hypertension usingthese OHT specific antibody patterns.

Furthermore, these antibody repertoires shown in FIGS. 2 and 3 areanalyzed by classification procedures (multivariate statistics andartificial neural networks).

In the following the calculation procedures used in this Example isdescribed. However, they can change slightly after inclusion of morepatients or moving to other technologies than Western blotting.

Using analysis of discriminance the most important molecular weightregions are included in this classification approach. To be included,the variables must show a statistically significant difference (P<0.05)between glaucoma and no glaucoma. 40 variables are included in theapproach. These variables (molecular weight regions) are used as inputneurons in an artificial neural network. The three layer feedforwardnetwork (MLFN) with the backpropagation training algorithm is used. Thenetwork has one output neuron which can have two states: glaucoma or noglaucoma. The network is allowed to prune those variables that do nothelp to distinguish between glaucoma or no glaucoma. The data vectors ofall patients are randomly divided into a test and training group. Thetraining groups are used to setup a neural network which is optimized tofind rules in the complex antibody patterns to detect glaucoma from theimmunological noise of naturally occurring antibodies. The test set,which is not included during training, is used to test if the neuralnetwork is able to detect glaucoma patterns form the patient group.Because the analysis of discriminance can demonstrate that the patternsof NTG and POAG are specific and different from each other, two neuralnetworks are built: the first one searching for NTG patterns and thesecond searching for POAG patterns. The output of both nets are combinedto detect glaucoma patterns in general. The algorithm uses those regionsof molecular weight that are found to be the most important todistinguish between the groups determined by the sensitivity analysis ofartificial neural networks. The analysis includes regions of alreadyknown antibodies, regions of unknown antibodies not described before,and especially regions where the reactivities in glaucoma patients aresignificantly lower than in healthy subjects.

After training, the following antigen-antibody reactivities (in kDa) areincluded: 11.2, 18.5, 9.8, 63.5, 28.9, 193, 12.1, 114.4, 110.4, 152,68.1, 133.6, 48, 157, 174, 19.1, 25, 27, 60.1, 197, 163, 20, 169, 7.8,186, 105, 14, 15.6, 142, 13, 42, 26.5, 23.4, 65, 57, 21.6, 88.3, 39, 10,36, 179.8, 78. The regions are ranked according to the importance on thedecision of the neural networks.

After training, the test procedure is performed using the test data set.83.5% of glaucoma patients and 85.2% of healthy subjects are correctlyclassified. This is equivalent to a sensitivity of 83.5% and aspecificity of 85.2%. Using antibody patterns to classify glaucomapatients, a responder operating characteristic (ROC) analysis iscalculated and an r-value of approximately 0.92 is found, which isindicative of a very good diagnostic test (FIG. 4).

To test the performance of the neural network, 165 non-glaucoma patientswith other ocular diseases (e.g. age dependent macular degeneration) areadditionally included in the analysis (CTRL2). The inclusion of thosepatients with other ocular disorders slightly decreases the AUC r of theROC curve to 0.84. Considering that now 524 patients are included in thestudy and the test is solely based on the antibody patterns withoutknowledge of any other clinical parameters of patients, this is again avery good diagnostic test.

Protein Chip

The autoantibodies in the sera of patients are coupled to Ciphergen IDMbeads and subsequently incubated with ocular antigens from retina oroptic nerve. After elution from the beads, the antigens can be analyzedby a time-of-flight mass spectrometer using Seldi-TOF surfaces. Usingthis approach, a complex pattern of antigen-antibody reactions can befound in the sera of patients. In FIG. 5, those group profiles ofantibody reactions are demonstrated for some molecular weight regionsfor glaucoma patients (GL), healthy subjects (CTRL), and a negativecontrol without antigens (NULL).

REFERENCES

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1. A method for the diagnosis of glaucoma, characterized in that in afirst step, autoantibodies against ocular antigens are detected andmeasured in body fluids of an individual, and, in a second step, theautoantibody pattern is correlated with corresponding patterns ofhealthy individuals and glaucoma patients.
 2. The method according toclaim 1 wherein the ocular antigens are retinal antigens, optic nerveantigens, optic nerve head antigens, trabecular meshwork antigens, uvealantigens, or a mixture of such antigens.
 3. The method according toclaim 2 wherein the ocular antigens are retinal antigens or optic nervehead antigens or a mixture thereof.
 4. The method according to claim 1wherein the body fluid is serum, tears, saliva, urine, aqueous humour,or vitreous body of the eye.
 5. The method according to claim 4 whereinthe body fluid is serum or tears.
 6. The method according to claim 4wherein the body fluid is serum.
 7. The method according to claim 1wherein the autoantibody pattern consists of at least 10 autoantibodies.8. The method according to claim 7 wherein the autoantibody patternconsists of at least 20 autoantibodies.
 9. The method according to claim7 wherein the autoantibody pattern consists of at least 30autoantibodies.
 10. The method according to claim 1 wherein theautoantibodies are detected and measured in a Western blot assay,chemiluminescence assay, ELISA, or RIA.
 11. The method according toclaim 10 wherein the autoantibodies are detected and measured in aWestern blot assay.
 12. The method according to claim 1 wherein theautoantibodies are detected and measured on a protein chip array usingsurface-enhanced laser desorption/ionization (SELDI) or matrix assistedlaser desorption/ionization (MALDI) mass spectrometry techniques. 13.The method according to claim 12 wherein the autoantibodies are detectedand measured on a protein chip array using surface-enhanced laserdesorption/ionization (SELDI) mass spectrometry technique.
 14. Themethod according to claim 1 wherein the autoantibodies are detected andmeasured by incubating protein-A chips with sera of individuals,treating said protein-A chips with a solution of ocular antigens,separating ocular antigens bound by autoantibodies on said protein-Achips by their molecular masses, and detecting separated ocular antigensby mass spectrometry.
 15. The method according to claim 1 wherein theautoantibodies are detected and measured by binding autoantibodies insera of individuals to beads, treating said beads with a solution ofocular antigens, eluting ocular antigens bound by antigen-antibodyreaction from the beads, and analyzing eluted ocular antigens usingSELDI-TOF or conventional electrophoretical techniques.
 16. The methodaccording to claim 1 wherein the technique to generate the autoantibodypattern is based on digital image detection, processing, and analysis.17. The method according to claim 1 wherein autoantibodies are detectedand measured in an individual's serum.
 18. The method according to claim17 wherein the change in the antibody pattern over time is used toassess the progression and/or severeness of glaucoma.
 19. A method ofcomparison of complex autoantibody patterns by calculation wherein apattern of autoantibodies against ocular antigens of an individual iscompared with a pattern of autoantibodies against ocular antigens ofhealthy individuals and with a pattern of autoantibodies against ocularantigens of glaucoma patients.
 20. The method of comparison according toclaim 19 wherein the pattern of autoantibodies against ocular antigensof glaucoma patients is the autoantibody pattern of patients withprimary open-angle glaucoma or of patients with normal tension glaucoma.21. The method of comparison according to claim 19 wherein thecalculation is based on artificial neural network technique.
 22. Amethod for assessing an individual's risk for developing glaucoma withor without an elevated intraocular pressure, characterized in that in afirst step, autoantibodies against ocular antigens are detected andmeasured in body fluids of the individual, and, in a second step, theautoantibody pattern is correlated with corresponding patterns ofhealthy individuals and of glaucoma patients.
 23. A kit for thediagnosis of glaucoma according to claim 1, comprising a ready-to-useocular antigen mixture and chemicals and materials needed to perform thebiochemical analysis.
 24. The kit according to claim 23 wherein thechemicals and materials are suitable for conventional Western blottingtechnique.
 25. The kit according to claim 23 wherein the chemicals andmaterials are suitable for the SELDI-TOF technique.
 26. The kitaccording to claim 23 comprising biochips.